StegoHound: A Novel Multi-Approaches Method for Efficient and Effective Identification and Extraction of Digital Evidence Masked by Steganographic Techniques in WAV and MP3 Files

TL;DR

This paper introduces StegoHound, a multi-approach automation method that improves the detection and extraction of hidden evidence in WAV and MP3 audio files, enhancing digital forensics efficiency and accuracy.

Contribution

The paper presents a novel multi-approach automation technique that outperforms standard methods in detecting steganography and cryptography in audio files, especially large ones.

Findings

Enhanced detection coverage and accuracy in audio steganography

Effective automation reduces manual analysis complexity

Good balance between detection efficiency and false positives/negatives

Abstract

Anti-forensics techniques particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice. This paper advances the automation of hidden evidence extraction in the context of audio files by proposing a novel multi-approaches method which enables the correlation between unprocessed artefacts, indexed and live forensics analysis and traditional Steganographic and Cryptographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation detecting and extracting hidden artefacts in WAV and MP3 audio files by comparing it to standard industry systems. This work advances the current automation in extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations, the proposed multi-approaches demonstrated a clear enhancement in terms of coverage and accuracy notably on large audio files (MP3 and WAV) for which the manual forensics analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present) but overall achieve a good balance between efficiently and effectively detecting hidden evidence and minimising the false negative which validates its reliability.